{"id":"W6889168319","doi":"10.25545/kbjhvd/q5yttb","title":"Raw Data 3.rar","year":2024,"lang":"en","type":"dataset","venue":"UNB Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Raw material; Raw data; Product (mathematics); Production (economics); Data collection","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.0009828942,0.0008469975,0.0007066621,0.0007387705,0.0001251258,0.0004801117,0.008203759,0.000592717,0.0458848],"category_scores_gemma":[0.0007818834,0.000819472,0.0001220695,0.0008458397,0.0002912411,0.001090748,0.01132084,0.001622526,0.9755222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000253602,"about_ca_system_score_gemma":0.0005998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129759,"about_ca_topic_score_gemma":0.001882809,"domain_scores_codex":[0.9950109,0.000169871,0.0006200269,0.00231246,0.001079988,0.0008068006],"domain_scores_gemma":[0.9818054,0.0001417643,0.0002728954,0.01738031,0.0000613772,0.0003382297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004315502,0.0001143976,4.673088e-7,0.0004405724,0.000355203,0.00182672,0.000008318876,0.000002874165,0.0000123152,0.00002662044,0.9970673,0.0001020482],"study_design_scores_gemma":[0.0004122061,0.0000307048,7.49566e-7,0.0003266011,0.001232584,0.00009084331,0.00004576578,0.0002266449,0.000005271209,0.00005665583,0.9966911,0.0008808831],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[5.81271e-7,0.0001175505,0.000002540936,0.00002474732,0.004405725,0.0004874442,0.993781,0.0004654455,0.000714916],"genre_scores_gemma":[2.03562e-7,0.0004878537,0.0003647046,0.0003455714,0.001105619,0.00001754128,0.9956252,0.0002754565,0.001777835],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9296374,"threshold_uncertainty_score":0.9994256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06133299856143031,"score_gpt":0.3244703467114607,"score_spread":0.2631373481500304,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}